Hybrid parametric/smooth inversion of electrical resistivity tomography data
The standard smooth electrical resistivity tomography inversion produces an estimate of subsurface conductivity that has blurred boundaries, damped magnitudes, and often contains inversion artifacts. In many problems the expected conductivity structure is well constrained in some parts of the subsur...
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ftdatacite:10.48550/arxiv.2107.10354 2023-05-15T17:57:52+02:00 Hybrid parametric/smooth inversion of electrical resistivity tomography data Herring, Teddi Heagy, Lindsey J. Pidlisecky, Adam Cey, Edwin 2021 https://dx.doi.org/10.48550/arxiv.2107.10354 https://arxiv.org/abs/2107.10354 unknown arXiv https://dx.doi.org/10.1016/j.cageo.2021.104986 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY Geophysics physics.geo-ph FOS Physical sciences article-journal Article ScholarlyArticle Text 2021 ftdatacite https://doi.org/10.48550/arxiv.2107.10354 https://doi.org/10.1016/j.cageo.2021.104986 2022-03-10T14:06:23Z The standard smooth electrical resistivity tomography inversion produces an estimate of subsurface conductivity that has blurred boundaries, damped magnitudes, and often contains inversion artifacts. In many problems the expected conductivity structure is well constrained in some parts of the subsurface, but incorporating prior information in the inversion is not a trivial task. In this study we developed an electrical resistivity tomography inversion algorithm that combines parametric and smooth inversion strategies. In regions where the subsurface is well constrained, the model was parameterized with only a few variables, while the rest of the subsurface was parameterized with voxels. We tested this hybrid inversion strategy on two synthetic models that contained a well constrained highly resistive or conductive near-surface horizontal layer and a target beneath. In each testing scenario, the hybrid inversion improved resolution of feature boundaries and magnitudes and had fewer inversion artifacts than the standard smooth inversion. A sensitivity analysis showed that the hybrid inversion successfully recovered subsurface features when a range of regularization parameters, initial models, and data noise levels were tested. The hybrid inversion strategy can potentially be expanded to a range of applications including marine surveys, permafrost/frozen ground studies, urban geophysics, or anywhere that prior information allows part of the model to be constrained with simple geometric shapes. Article in Journal/Newspaper permafrost DataCite Metadata Store (German National Library of Science and Technology) |
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Geophysics physics.geo-ph FOS Physical sciences |
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Geophysics physics.geo-ph FOS Physical sciences Herring, Teddi Heagy, Lindsey J. Pidlisecky, Adam Cey, Edwin Hybrid parametric/smooth inversion of electrical resistivity tomography data |
topic_facet |
Geophysics physics.geo-ph FOS Physical sciences |
description |
The standard smooth electrical resistivity tomography inversion produces an estimate of subsurface conductivity that has blurred boundaries, damped magnitudes, and often contains inversion artifacts. In many problems the expected conductivity structure is well constrained in some parts of the subsurface, but incorporating prior information in the inversion is not a trivial task. In this study we developed an electrical resistivity tomography inversion algorithm that combines parametric and smooth inversion strategies. In regions where the subsurface is well constrained, the model was parameterized with only a few variables, while the rest of the subsurface was parameterized with voxels. We tested this hybrid inversion strategy on two synthetic models that contained a well constrained highly resistive or conductive near-surface horizontal layer and a target beneath. In each testing scenario, the hybrid inversion improved resolution of feature boundaries and magnitudes and had fewer inversion artifacts than the standard smooth inversion. A sensitivity analysis showed that the hybrid inversion successfully recovered subsurface features when a range of regularization parameters, initial models, and data noise levels were tested. The hybrid inversion strategy can potentially be expanded to a range of applications including marine surveys, permafrost/frozen ground studies, urban geophysics, or anywhere that prior information allows part of the model to be constrained with simple geometric shapes. |
format |
Article in Journal/Newspaper |
author |
Herring, Teddi Heagy, Lindsey J. Pidlisecky, Adam Cey, Edwin |
author_facet |
Herring, Teddi Heagy, Lindsey J. Pidlisecky, Adam Cey, Edwin |
author_sort |
Herring, Teddi |
title |
Hybrid parametric/smooth inversion of electrical resistivity tomography data |
title_short |
Hybrid parametric/smooth inversion of electrical resistivity tomography data |
title_full |
Hybrid parametric/smooth inversion of electrical resistivity tomography data |
title_fullStr |
Hybrid parametric/smooth inversion of electrical resistivity tomography data |
title_full_unstemmed |
Hybrid parametric/smooth inversion of electrical resistivity tomography data |
title_sort |
hybrid parametric/smooth inversion of electrical resistivity tomography data |
publisher |
arXiv |
publishDate |
2021 |
url |
https://dx.doi.org/10.48550/arxiv.2107.10354 https://arxiv.org/abs/2107.10354 |
genre |
permafrost |
genre_facet |
permafrost |
op_relation |
https://dx.doi.org/10.1016/j.cageo.2021.104986 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.48550/arxiv.2107.10354 https://doi.org/10.1016/j.cageo.2021.104986 |
_version_ |
1766166378612523008 |